Over the past few years, deep neural networks (DNNs) have achieved state-of-the-art performance on a wide range of learning tasks, such as image classification, object detection, segmentation and image captioning [1]}, [2]}. All of these breakthroughs, however, are achieved in the framework of discriminative models, which are known to be exposed to several critical issues, such as adversarial examples [3]}, calibration of uncertainty [4]} and out-of-distribution detection [5]}. Prior works have shown that generative training is beneficial to these models and can alleviate some of these issues at certain levels [6]}, [7]}. Yet, most recent research on generative models focus primarily on qualitative sample quality [8]}, [9]}, [10]}, and the discriminative performances of state-of-the-art generative models are still far behind discriminative ones [11]}, [12]}, [13]}.
<FIGURE>